Textual Analysis Research Articles

Without morals in a moral world—the expanding moral circle of The Witcher 3: The Wild Hunt

As video and computer games get increasingly more advanced, the immersive experience for the player of these games also follows this trajectory. In these games, one aspect that has been explored to the greatest extent is that of choice and player agency. Often, these choices and acts are of a moral character, whether it be choosing what to do on an ambiguously formulated questline with multiple branches or how to approach different creatures within the game world. The game The Witcher 3: The Wild Hunt provides a case of a game that uses many mechanisms to explore issues related to moral and morality. Through a qualitative textual analysis of the game, this article explores how players are faced with the game’s moral order. Utilizing Singer’s idea of the expanding moral circle, this article explores the game world’s establishing of moral issues, including who and what are included in the expanding circle and in what way this is conveyed within the game. Relying on the dual nature of the gameplay, where players are role-playing as the Witcher Geralt of Rivia as well as experiencing what unfolds in the game in front of a screen, this article discusses how these moral orders–the one in the game world and the one in “real” life—intertwine the moral underpinnings of a digital game. This article also discusses how encounters with non-human animals often leave the player with very few choices regarding which to act, which is not presented as moral issues at all. Furthermore, this article discusses further development of the theoretical framework as a possible vantage point from where to understand morality and how it emanates in different game worlds.

SceneKG: A Geo‐Scene Based Spatiotemporal Knowledge Representation Framework Considering Geo‐Processes

ABSTRACTCurrent geographic knowledge graph research mainly focuses on static knowledge coupling through analysis of web text and maps, but its knowledge expression model fails to incorporate knowledge mechanisms such as process evolution and interactions. The geo‐scene is a synthesis of various geographic elements which prescribes spatial, temporal, semantic, attribute, and interrelationships. It has demonstrated its utility in facilitating geocomputation, spatiotemporal simulation and reasoning related to geo‐process. Therefore, developing a geo‐scene‐based knowledge graph is significant to improve the ability of spatiotemporal knowledge representation and service. In this study, we first analyze the basic characteristics of geo‐scenes and try to emphasize the evolution of geo‐scenes through redefining the ontologies ‘event’, ‘process’ and ‘state’. To describe the dynamic nested structure of geo‐scenes, we propose a geo‐scene knowledge representation framework, and illustrate the representation of a geo‐scene and its inner relationships. Moreover, to keep the integrity, interaction and dynamic of geo‐scenes, we develop a event‐process centered geo‐scene knowledge organization method. Furthermore, we have tested the utility of our proposed representation by the Beijing 731 heavy rainstorm. The case study represents heavy rainstorm to demonstrate the complicated structure of the disaster scene, and the support for precipitation process simulation, early warning information organization, and disaster situation information query. The proposed representation provides an idea for the construction of geo‐dynamic knowledge graphs, encodes both static and dynamic geographic knowledge, makes explicit the evolution and interaction knowledge in geo‐scenes, and expands the application service of geo‐scene knowledge graph in the era of GeoAI.

Examining the Effects of Health Work Shortages and Deployment in Integrated Programme in Bihar, India: AnInstitutional Ethnography.

The integration of health programms in low-resource settings is a common approach aimed at providing quality and cost-effective healthcare services. However, programme integration can unintentionally impact service provision and disrupt the overall organisation of service delivery. In this paper, I examine the integrated programme for non-communicable diseases (NCDs) and explore the challenges related to the deployment of health workers. Specifically, I investigate the institutional processes that shape the work of health workers and the working conditions in district hospitals. To explore the social organisation of the implementation of the integrated NCD programme, I employed institutional ethnography as a research method. Data collection involved interviews, participant observation, and document reviews. Data analysis was conducted using social mapping, analytical writing, and textual analysis methods. The findings of the study suggest that due to staff shortages and a lack of doctors, health workers are continuously subjected to deployment and often required to undertake multiple tasks and responsibilities without any incentives or non-monetary benefits. Administrators perceive health workers in integrated programs as additional human resources available for use in district hospitals. However, the removal of health workers from integrated programs can have negative implications for service delivery and patients' health outcomes. Finding of the study suggests that it is recommended that administrators improve the staff recruitment process and promptly address the grievances of health workers to enhance healthcare delivery.

Processo de aprendizagem na fé

The dialogue between theology and pedagogy builds the basis for the development of the learning process from the perspective of faith. It is not faith that grows, but rather people who mature in attitudes that correspond to the hope they confess, concrete attitudes in their daily lives. Considering the cognitive, affective and attitudinal dimensions of the human being, this research has as its main objective to describe learning that is integral, significant and continuous, from the perspective of faith. Therefore, in addition to bibliographical research, an exegetical analysis of biblical texts that support each of these learnings was carried out. It is important to consider the existential context of students, establishing the link between faith and life, promoting integral, significant learning and continuous formation in the faith, that is, in each phase of life. The task of continuous formation in the faith is the responsibility of all people who participate in life in the Christian community, evidencing the general priesthood of all people who believe in an effective and efficient way. The Church of Jesus Christ is concerned with the catechumenate, with ongoing discipleship, with the formation in the faith of children, adolescents and young people, as well as adults and the elderly, planning and implementing in a conscious and responsible way the integral and significant learning in the process of formation in the faith throughout life.

Nonlinear Deep Learning Framework for Precise Sentiment Classification in Textual Data

The study presents the Nonlinear Deep Learning Framework for Precise Sentiment Classification (NPSC), focusing on improving sentiment analysis in text data. The model addresses the challenge of capturing complex and hidden sentiment patterns using a combination of BERT embeddings, Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), and an attention mechanism. This setup helps in identifying subtle relationships within text, enabling more precise sentiment predictions. NPSC tackles common issues faced by existing models that struggle with detailed text analysis. Using the IMDB movie review dataset, the approach relies on BERT to convert words into dense vectors, BiLSTM for capturing context from both directions, and CNN to extract key local features. An attention layer highlights important words, refining the sentiment detection process. The integration of these components in a nonlinear fusion layer allows for enhanced feature interaction, leading to accurate classification through a final softmax layer. The findings show that NPSC performs better across metrics like accuracy, precision, recall, and F1-score. Ablation studies reveal the impact of each component, demonstrating their role in enhancing the model’s effectiveness. Error analysis shows how NPSC manages mixed sentiments and challenging text patterns. The study highlights NPSC’s potential as a reliable method for sentiment analysis, contributing to better handling of complex textual data.

Dotless Arabic Text for Natural Language Processing

Abstract This article introduces a novel representation of Arabic text as an alternative approach for Arabic NLP, inspired by the dotless script of ancient Arabic. We explored this representation through extensive analysis on various text corpora, differing in size and domain, and tokenized using multiple tokenization techniques. Furthermore, we examined the information density of this representation and compared it with the standard dotted Arabic text using text entropy analysis. Utilizing parallel corpora, we also drew comparisons between Arabic and English text analysis to gain additional insights. Our investigation extended to various upstream and downstream NLP tasks, including language modeling, text classification, sequence labeling, and machine translation, examining the implications of both the representations. Specifically, we performed seven different downstream tasks using various tokenization schemes comparing the standard dotted text with dotless Arabic text representations. Performance using both the representations were comparable across different tokenizations. However, dotless representation achieves these results with significant reduction in vocabulary sizes, and in some scenarios showing reduction of up to 50%. Additionally, we present a system that restores dots to the dotless Arabic text. This system is useful for tasks that require Arabic texts as output.

Improving corporate profitability from the inside out with environmental performance: an empirical analysis based on Chinese listed company data

This study uses data from listed companies in China from 2008 to 2021, constructs a comprehensive evaluation of corporate environmental performance from the perspective of internal efficiency and externalities based on the text analysis method, and analyzes the impact and mechanism of environmental performance on profitability. In addition, we find that improving environmental performance can significantly increase profitability, especially if the company tackles pollution at the source. Furthermore, the impact of environmental performance on profitability tends to be driven by external markets. Obtaining environmental certifications, awards, and green patents can provide positive feedback and significantly increase profitability. In addition, the mechanism analysis shows that environmental performance affects firm profitability by improving green technology innovation, reducing financing constraints and increasing operating income. Finally, the results of the heterogeneity analysis show that the profitability-enhancing effect of environmental performance is stronger in non-state-owned firms, in clean industries firms and in regions with carbon emission trading.

Institutional Trading and Managers' Complex Disclosure: Evidence From Conference Calls

ABSTRACTThis study investigates the impact of managerial linguistic disclosures on institutional trading behaviors during earnings conference calls from 2006 to 2016. Utilizing the linguistic Fog decomposition methodology on textual analysis, it analyzes how the linguistic complexity of managerial communication influences institutional ownership changes. Findings indicate that both obfuscation and informativeness of managerial discourse significantly affect institutional trading, with effects varying based on the informativeness of post‐call analyst reports. This impact is more pronounced under conditions of earnings quality uncertainty, suggesting a heightened reliance on managerial cues by institutional investors during such periods. Additionally, the study differentiates responses among investor types: transient investors are more sensitive to linguistic cues, while dedicated and quasi‐indexer investors focus on the informational content of disclosures. These insights enhance understanding of how managerial communication and analyst report informativeness shape institutional trading behaviors through linguistic complexity's influence on institutional ownership.

The role of the insider translator in conservation and development: comparing multilingual (auto)ethnobotanical books from Tanzania, Thailand, and Taiwan

Abstract This article compares recent multilingual (auto)ethnobotanical books from Tanzania, Thailand, and Taiwan in terms of the role that the “insider translator” might play in linguistic, cultural, or environmental conservation or development. The books were motivated by similar concerns, but differed in the backgrounds of the authors, including translators and compilers. How did the backgrounds of the authors – as cultural outsiders or insiders – condition the form and content of the (auto)ethnobotanical books? What approach to authorship might be most effective for achieving the intended aims of the (auto)ethnobotanists? Based on textual analysis, interviews, and fieldwork, the main findings are as follows. The interlingual translation in the Tanzanian and Thai cases was unidirectional out of the local language, because the compilers, who were outsiders, were more concerned about the conservation of endangered languages, cultures, and environments. There was no division of labor between compiler and translator in the Taiwanese case, and the translation was bidirectional, because the authors were committed to the development of their language. It stands to reason that an autoethnobotanical effort, one made by cultural insiders, to conserve traditional plant knowledge or develop an ancestral language would be more effective than an ethnobotanical one; but the long-term effectiveness of any of the multilingual (auto)ethnobotanical books needs further comparative research.

Application of Content Analysis in Research of Political Discourse

Content analysis is one of the most frequently applied methods for the research of various forms of discourse, including political discourse. As a research technique, it was developed in studies on communication and is now frequently used in linguistics, for example, in text analysis, systematic studies of written text or transcribed speech, as well as in research on nontextual message content. The following article deals with the application of content analysis in research into political discourse and discusses the crucial concepts of the research method. Specifically, it describes the manifest and latent content of political texts and presents two notions particularly useful in the evaluation of content analysis, i.e., ‘reliability’ and ‘validity.’ Additionally, two broad approaches to content analysis are discussed. The first is qualitative content analysis, and the second is quantitative content analysis.

SWOT analysis as a tool for selecting text materials for the formation of English communicative competence of students of non-linguistic specialties

The study is devoted to the implementation of SWOT analysis as an auxiliary criterion for the selection of text material in the formation of English communicative competence for students of non-linguistic specialties. The article examines the concept of communicative competence, as a complex phenomenon of students’ availability of appropriate abilities and skills for high-quality foreign language communication. The main groups of criteria for the selection of text material have been outlined, including 1) subject-scientific ones; 2) linguistic stylistic; 3) methodological; 4) person-oriented. The concept of SWOT analysis has been summarized and the components of the matrix of this analysis have been analyzed, namely: the factors of positive influence of the internal environment are – Strengths, the negative factors – Weaknesses, and the factors of external influence are Opportunities, and the negative ones are Threats. The use of SWOT analysis, based on the classic qualitative criteria for text selection has been proposed as follows: Strengths – linguistic stylistic criteria of variety of types and genres and professionally oriented lexical units; Weaknesses – methodological criteria of authenticity, accessibility, adaptability, etc.; Opportunities – subject-scientific criteria of novelty, informational value and the presence of paralinguistic components; Threats are person-oriented criteria of relevance and practical value. An analysis of the English-language text has been carried out according to the proposed SWOT-analysis model and qualitative criteria for the selection of text material have been highlighted as well.

Mental health care needs of caregivers of people with Alzheimer's disease from online forum analysis.

Informal caregivers of people with Alzheimer's disease and related dementias (ADRD) are at risk of poor mental health. This study aimed to investigate the feasibility and validity of studying caregivers' mental stressors using online caregiving forum data (March 2018-February 2022) and natural language processing and machine learning (NLP/ML). NLP/ML topic modeling generated eight prominent topics, which we compared with qualitatively defined themes and the existing caregiving framework to assess validity. Among a total of 60,182 posts, 5848 were mental distress-related; for the ADRD patients (symptoms, medication, relocation, care duty share, diagnosis, conversation strategy) and the caregivers (caregiving burden and support). While we observed novel topics from NLP/ML-defined topics, mostly those were aligned with the existing framework. For feasibility assessment, qualitative title screening was done. The findings shed new light on the potential of NLP/ML text analysis of the online forum for informal caregivers to prepare tailored support for this vulnerable population.

Sentiment analysis of economic text: A lexicon‐based approach

AbstractWe propose an economic lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: (1) to have a wide coverage of terms used in documents discussing economic concepts, and (2) to provide a human‐annotated sentiment score in the range [−1, 1]. We illustrate the use of the EL in the context of a simple sentiment measure and consider several applications in economics. The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment.

Lost in translation: error analysis of texts translated from Arabic into English by Saudi translators

Biographic translation is crucial in today’s globalized world to preserve histories or shed light on the lives of people who influenced their nations or the world. The literature on biographic translation and the factors hindering such translation in an English as a foreign language (EFL) setting, especially in the Saudi context, is relatively limited. In the present study, the author analyzed the errors made by Saudi EFL learners when they translated a biography from Arabic into English. The data were drawn from a short text translated by female final-year university students as part of their course evaluations. The author conducted a qualitative analysis and classified errors according to Pospescu’s taxonomy. The results showed that the students committed all the three error types: linguistic, comprehensive, and translation. Within these three types, the students made diverse errors, ranging from misusing tenses to misspelling words. Consequently, their overall translations were unsatisfactory. This finding is helpful for both learners and educators, as the identified translation errors can be used as teaching aids in translation classes. The findings put emphasis on the importance of practice and prompt feedback as essential aids educators can use to support their students during their translation learning.

Representation of Violence Against Female Characters in Indonesian Horror Novels

This study explores the representation of violence against female characters in Indonesian horror novels, examining how these portrayals reflect societal views on gender and power. By analyzing three popular novels, the research focuses on types of violence—physical, psychological, and symbolic—that female characters experience, aiming to uncover how these depictions critique patriarchal norms. Employing a descriptive qualitative approach, the study uses textual analysis alongside a feminist narrative framework to interpret recurring themes and patterns within these narratives. Findings reveal that, while female characters are often portrayed as victims, they also demonstrate moments of resistance and agency, challenging traditional roles and societal expectations. This ambivalent portrayal highlights both the vulnerability and resilience of women, offering insights into the complex dynamics of gender in Indonesian culture. The study concludes that Indonesian horror novels serve as a powerful medium for social critique, using horror to comment on issues of gender-based violence and the potential for women to disrupt oppressive structures. This research emphasizes the role of literature in reflecting and challenging societal norms, particularly regarding the representation of women.

Whose menopause revolution? Investigating the UK’s ‘Davina effect’ and the contemporary menopause market

Having made two documentaries about menopause, broadcast on Channel 4 in the UK 2021 and 2022, enduringly popular British TV presenter Davina McCall’s films are perceived to have had such a social impact that she now has the rare distinction of having an ‘effect’ named after her. The first film, Sex, Myths and the Menopause, is credited with diminishing the stigma historically surrounding menopause, while extolling the benefits of Hormone Replacement Therapy (HRT) for some to treat menopause symptoms. Hence, when requests for HRT spiked after the first film, ‘the Davina effect’ was adopted by the media to explain the widespread ensuing HRT shortage. This article is not focused on interrogating the films themselves but unpacking the media narrative that followed in the uses of the term ‘the Davina effect’. Its corpus comprises UK press coverage of the ‘menopause revolution’ and of the Davina documentaries; textual analysis of the films; and testimonies and research drawn from the medical and health professions, while utilising existing media and cultural studies paradigms to provide conceptual context, situating the ‘Davina effect’ as the latest novel entry in a long-standing history of media effects debates. While the films have been praised for bolstering a new era of menopause awareness, this article considers how ‘the Davina effect’ instantiates and services how the so-called ‘menopause revolution’ (Gordon, 2021) has evolved largely in neoliberal terms, regularly centring the need to respond to the perceived crises of middle-class white women, rather than a more intersectional and inclusive conception of menopause. Furthermore, it apportions knowledge, fear and blame around HRT shortages in a manner which deflects attention from the larger economic, political and cultural contexts that have nurtured the ensuing alarm. Yet, importantly, the Davina effect may also facilitate a transgressive image of menopausal women as determinedly contesting their marginalisation.

Can the MD&A Tone of Listed Firms’ Annual Reports Predict Their Future Performance? Empirical Evidence Based on Machine Learning Text Analysis*

AbstractBased on a sample of listed companies in China's A‐share market between 2009 and 2019, we analyze the tone of the Management's Discussion and Analysis (MD&A) section of 15 743 annual reports through machine learning. The MD&A tone displays a significantly positive association with the firm's future financial performance, proving that the textual tone of MD&A includes forward‐looking aspects regarding management's intentions for the company's prospects, and investors can use it to forecast the firm's future performance. Additionally, higher levels of internal control quality, stronger analyst following, and higher holdings by institutional investors can enhance the correlation between the MD&A tone and the company's future performance.

User perceptions of aspects related to the use of the activity chair: A descriptive cross-sectional study

Introduction: Little is known about the use and aspects impacting the use of the activity chair, which is a device supporting seated activities in everyday life. The aim was to understand (a) purpose of provision, (b) frequency and magnitude of use, (c) barriers impacting use, (d) instruction provided and (e) overall satisfaction with the activity chair. Method: This is a cross-sectional study involving 141 adult activity chair users. Data were collected through telephone interviews using a study-specific questionnaire with 21 items. Descriptive statistics were used to summarise the quantitative results, and data-driven text analysis was conducted to analyse open-ended question responses. Results: Most respondents (87%) were provided the activity chair for kitchen work, 87% used the device on a daily basis, 75% used the device for additional activities beyond the provision purpose, 65% experienced barriers impacting use, instructions varied, with 28% receiving none and 16% had received activity-based training, 85% were satisfied with the activity chair. Conclusion: The activity chair appears to be regularly utilised and appreciated by respondents. However, improved device provision, particularly concerning activity-based training in relevant context and systematic follow up, may be required to reduce barriers impacting use. Findings emphasise the importance of learning from users.

ChatGPT for complex text evaluation tasks

AbstractChatGPT and other large language models (LLMs) have been successful at natural and computer language processing tasks with varying degrees of complexity. This brief communication summarizes the lessons learned from a series of investigations into its use for the complex text analysis task of research quality evaluation. In summary, ChatGPT is very good at understanding and carrying out complex text processing tasks in the sense of producing plausible responses with minimum input from the researcher. Nevertheless, its outputs require systematic testing to assess their value because they can be misleading. In contrast to simple tasks, the outputs from complex tasks are highly varied and better results can be obtained by repeating the prompts multiple times in different sessions and averaging the ChatGPT outputs. Varying ChatGPT's configuration parameters from their defaults does not seem to be useful, except for the length of the output requested.

Pop Culture Media as Curricular Text: Designing an Interdisciplinary Undergraduate Seminar Using Abbott Elementary

Abbott Elementary, the Emmy-award winning mockumentary-style comedy television show, chronicles the day-to-day efforts of a group of teachers in a Philadelphia public elementary school who, despite the odds stacked against them, are determined to help their students succeed. With humor and heart, the show also uncovers some of the larger policy issues at play in U.S. urban public education, such as teacher shortages, school funding, and school choice. This essay describes the development of an interdisciplinary Honors seminar for undergraduate students at a large, public university in the Midwest U.S. that used episodes of Abbott Elementary as a central curricular text to support students in analyzing current policy issues in urban education. Drawing on Giroux’s concept of media as public pedagogy, this essay details how the course design employed a critical intersectional multiculturalism cultural studies approach to create opportunities for students to engage in a multiperspectival analysis, including (1) an understanding of political culture, (2) textual analysis, and (3) audience reception. Implications and recommendations are made for selecting and integrating popular culture media as curricular text in interdisciplinary coursework.